Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional...
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Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.
A complicated neuro-developmental disorder called Autism Spectrum Disorder (ASD) is abnormal activities related to brain development. ASD generally affects the physical impression of the face as well as the growth of ...
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Twitter is a popular microblogging-based online social network (OSN) that acts as a platform for users to express themselves and enrich public relationships. Monthly active users in twitter have reached approximately ...
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This research proposes a refined deep learning framework aimed at boosting the precision and efficacy of detecting surface imperfections in strip steel. This method integrates enhancement and simplification techniques...
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In the age of digital communication, social media platforms have become essential tools for interaction and information sharing worldwide. However, these platforms often face the challenge of managing negative and har...
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In recent times, the system's mathematical expression and operation have gained greater reach in engineering and mathematics. It is vital to solving more complex expressions and equations in a short time. The most...
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Autonomous navigation in dynamic environments poses significant challenges due to complex agent–environment interactions, real-time decision-making, and adaptability requirements. We propose the Reinforcement-Imitati...
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Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts researchers for finding the accurate tertiary structure of the protein...
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Even though various features have been investigated in the detection of figurative language, oxymoron features have not been considered in the classification of sarcastic content. The main objective of this work is to...
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This research work addresses issues related to developing an effective and efficient Animal Footprint, classification and tracking system. Successful attempts towards developing algorithmic models for animal segmentat...
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